Various works try to mix the benefits of MB and MF methods. For example, [1]} proposes the use of trajectory optimization for guiding the policy search by exploring high-reward regions.
In [2]}, the authors propose a MB method which learns an ensemble of models and optimizes a meta-policy objective over all models. [3]} proposes the initialization of MFRL algorithms from learned model dynamics, for combining the sample efficiency of MB methods with the task-specific performance of MF approaches.
Many works study the use of MB methods for accelerating the learning of MFRL. The latter can be extended into MB methods by sampling from a model (or model ensemble to increase stochasticity) [4]}, [5]}, [6]}. MBPO [7]} uses branched rollouts in an actor-critic setting to exploit the learned dynamics for model-based policy optimization. [8]} proposes an extension to MBPO, by performing model rollouts of specific horizons, while optimizing the policy objective with back-propagation through time.
